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About This Role
Location: Remote in USA
Job Summary:
As the Principal Architect for the AI \& Agentic Stack, you are a high\-impact consultative leader and trusted advisor responsible for the architectural integrity of our enterprise AI solutions. Your mission is to translate vision and complex business requirements into detailed technical blueprints for model selection, API \& data orchestration, and agentic logic. You act as a key bridge between sales and delivery, working closely with customers and Google channel partners to design scalable, governed, and high\-performing systems leveraging Gemini Enterprise and Vertex AI.
Responsibilities
- Consultative Solutioning \& Sales Support: Partner with sales to lead technical discovery, perform value\-engineering, and design tailored AI architectures that solve specific client business challenges in high\-stakes pursuits.
- Technical Blueprinting \& Delivery Oversight: Design end\-to\-end architectures for complex AI systems (RAG, multi\-agent systems) and provide technical governance during implementation to ensure delivery aligns with original architectural intent.
- Customer \& Channel Technical Liaison: Act as a subject matter expert for clients and Google's technical field teams, coordinating joint\-innovation pursuits and ensuring alignment with the latest Gemini and Vertex AI roadmaps.
- Gemini Enterprise \& Agentic Design: Lead the configuration of the Gemini Enterprise platform and architect agentic systems with advanced prompt engineering, iterative behavioral tuning, and robust human\-in\-the\-loop oversight.
- Prompt Engineering \& System Tuning: Develop and optimize advanced prompt strategies for the Gemini ecosystem, balancing autonomy and specific behavioral goals through iterative tuning and system instructions.
- MLOps \& Data Governance: Define standards for MLOps and data orchestration, ensuring AI models are securely integrated with corporate data estates and comply with GCP best practices.
- Technical Quality Control: Perform deep\-dive architectural reviews of AI projects to ensure adherence to Google Cloud best practices.
Required Qualifications:
- 12\+ years in technical architecture with a focus on AI, Data, Integration, and consultative leadership
- Expert\-level knowledge of Gemini Enterprise (formerly Agent space), Vertex AI, and Big Query ML
- Proven experience in customer\-facing roles, with the ability to influence CXO stakeholders and lead technical solutioning for AI\-driven business transformations
- Proven experience in Prompt Design \& Engineering (ideally with Gemini) and tuning LLM\-based systems for specific behaviors and safety constraints
- Deep understanding of Python\-based AI orchestration (Lang Graph, ADK, etc.)
Desired Qualifications:
- Google Professional Machine Learning Engineer certification.
- Experience with high\-performance computing (HPU/TPU) and specialized AI hardware on GCP
The base salary for this position is $155,000 \- $220,000, plus incentives that align with individual and company performance. Actual salaries will vary based on work location, qualifications, skills, education, experience, and competencies. Benefits available to eligible employees in this role include medical, dental, and vision insurance, comprehensive employee assistance program, 401(k) retirement plan, paid time off and holidays and paid learning days.
The deadline to apply for this position is: 06/05/2026\. This position is for an existing, immediate vacancy. We are currently seeking to fill this role with an individual who can start as soon as possible.
As part of the hiring process, candidates may be required to undergo background screening and identity verification, where permitted by applicable law and consistent with the requirements of the role. Certain verification processes used by the Company or its service providers may involve technologies that rely on biometric identifiers or biometric information, where permitted by law. If biometric identifiers or biometric information are collected, used, or stored, the Company will provide the legally required disclosures and obtain any required written consent prior to such collection, and will handle such information in accordance with applicable biometric privacy laws and Company policies.
Physical and Mental Requirements
The employee is regularly required to operate a computer, keyboard, telephone/headset, and/or other office equipment as essential functions of this position. Work is generally sedentary in nature.
Equal Employment Opportunity
Concentrix is an equal opportunity and affirmative action (EEO\-AA) employer. We promote equal opportunity to all qualified individuals and do not discriminate in any phase of the employment process based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy or related condition, disability, status as a protected veteran, or any other basis protected by law.
For more information regarding your EEO rights as an applicant, please visit the following websites:
- English: https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRights6\.12\.pdf
- Spanish: https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRightsSp6\.12\.pdf
Accommodation
Concentrix welcomes and encourages applications from candidates with disabilities and is committed to providing an inclusive recruitment process. If you require reasonable accommodation to participate in any stage of the application or interview process, please let us know. Requests may be made by contacting [email protected]. All information will be treated confidentially and used solely to facilitate your participation in the recruitment process.
Artificial Intelligence
As part of our recruitment process, we may use artificial intelligence (AI) tools to assist in the screening and/or assessment of job applicants. These tools could be used to evaluate resumes, applications, and other materials submitted to help us identify the best candidates for the role.
Work Authorization
In accordance with federal law, only applicants who are legally authorized to work in the United States will be considered for this position. Must reside in the United States or have a valid U.S. address for residence.
For further information on available work states and Equal Employment Opportunity as an applicant, please visit: https://jobs.concentrix.com/north\-america\-equal\-employment\-opportunity\-information/
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\#LI\-Remote
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Salary Context
This $155K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Concentrix, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $155K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Concentrix AI Hiring
Concentrix has 4 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span Austin, TX, US, AL, US, UT, US. Compensation range: $120K - $220K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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